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To create an AI governance policy, define what AI uses it covers, assign accountable owners, require teams to document and assess each use, set risk-based approval and safeguards, and monitor systems after deployment. The NIST AI Risk Management Framework (AI RMF) offers a voluntary structure for that work: Govern, Map, Measure, and Manage. It is guidance, not a law or a guarantee of compliance; legal requirements depend on where your organization operates, its sector, and its uses of AI.
Start with a framework, not a generic checklist
The NIST AI RMF is designed to help organizations manage risks that AI systems may pose to people, organizations, society, and the environment. Its four functions—Govern, Map, Measure, and Manage—can organize policy requirements and related procedures. Governance runs through the other functions rather than ending with policy approval, and risk management continues throughout a system’s lifecycle. See the NIST AI Risk Management Framework and the AI RMF Core.
NIST describes the AI RMF as voluntary, and says it is revising AI RMF 1.0, released on January 26, 2023. Check NIST’s framework page for the current status before adopting a particular version. The framework does not determine which laws apply to your organization. Use it as a flexible organizing tool, then have legal or compliance staff identify requirements for your operating locations, sector, and AI applications.
Build the policy in seven steps
1. Define what the policy covers
Set a clear boundary for covered systems and uses. Consider internally developed models, AI features embedded in existing software, third-party AI services, and business processes that rely on AI-generated outputs. Cover both acquiring a system and developing or materially changing one.
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State any exclusions precisely and explain who can approve them. Establish a way to revisit the scope when tools, capabilities, business uses, or applicable requirements change. Avoid a definition so narrow that teams can bypass review by using a vendor service or an AI feature bundled into another product.
2. Assign owners and decision rights
Name the executive sponsor and policy owner, and make clear who is accountable for each system or use. Specify who reviews proposals, who can approve them, who accepts residual risk, and where teams escalate concerns or incidents. The policy should distinguish advisory review from the authority to approve, restrict, or stop a use.
Bring in the functions relevant to your organization and applications. These may include legal, privacy, security, risk, procurement, technical teams, business owners, and people with knowledge of affected groups. For smaller organizations, one person may hold several responsibilities, but the responsibilities and escalation route should still be explicit.
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3. Require teams to map each use
Before a system is approved, require a short record of its intended purpose and operating context. The level of detail should reflect the use and its risk, but teams generally need to identify:
- The system, model, tools, components, suppliers, and business owner.
- Intended users, affected people, deployment setting, and decisions or tasks the system supports.
- Data used or produced, including sensitive data where relevant, and how outputs enter the workflow.
- Foreseeable changes, limitations, misuse, or other conditions that could alter the system’s effects.
This mapping gives reviewers the context to decide which risks matter and how much evidence is appropriate. The NIST AI RMF Playbook offers suggested actions and documentation practices; it is not a universal checklist that every organization must follow verbatim.
4. Assess risks and specify evidence
Require an assessment of risks and potential impacts in the actual context of use, not just a general statement that a system is safe or trustworthy. NIST identifies characteristics to consider, including validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, including management of harmful bias.
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For each use, set the evidence needed to support the assessment in proportion to its potential effects and the organization’s risk tolerance. Depending on context, evidence might address testing, data, performance limitations, human review, security, privacy, or effects on affected people. A single generic checklist cannot establish trustworthiness for every system or purpose.
5. Set approval gates and risk responses
Define when a use needs review and what an approval can require. Approval conditions might include safeguards, human oversight, restricted users or data, additional testing, or limits on where outputs may be used. Specify who can accept remaining risk and how that decision is recorded.
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6. Monitor use and manage change
Make approval the beginning of oversight, not its end. Assign responsibility for post-deployment monitoring, complaint and incident handling, and reassessment when a system, its data, its users, its purpose, or its deployment setting changes. Define who must be notified and who can initiate investigation or escalation.
Cover the full lifecycle: pre-design, design and development, deployment, use, and testing and evaluation. Set a schedule for reviewing the policy itself as well as triggers for out-of-cycle updates. Governance needs to adapt as systems and organizational needs change.
7. Add rules for generative AI
Address generative AI directly rather than assuming general rules answer every practical question. Specify whether employees may use external services, what information they may submit, when generated material needs human verification, and what review is required before content or outputs are used in consequential workflows. Tailor controls to your applications, data, suppliers, and affected people.
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NIST published its Generative AI Profile on July 26, 2024 as a cross-sector companion to AI RMF 1.0. It can inform generative-AI risk work, but it does not resolve every sector-specific or legal question.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turn policy statements into usable procedures
A policy should set expectations and decision rights; supporting procedures should tell employees how to meet them. For example, connect the policy to a system inventory, an intake and review process, risk assessment records, approval documentation, incident reporting, and change control. Make clear where employees can find these processes and whom to contact when a proposed use does not fit an existing category.
Use the same governance logic for internally developed and third-party systems, while adjusting the evidence you can reasonably obtain from a supplier. Record what is known, what remains uncertain, the conditions placed on use, and the person authorized to accept residual risk. This gives later reviewers a basis for reassessment instead of treating the original approval as permanent.
Check the policy before issuing it
- Can teams tell whether a specific tool or use is covered?
- Is there a named owner and a clear approval and escalation path?
- Does the review consider purpose, context, data, suppliers, users, and affected people?
- Are risk-based evidence, safeguards, residual-risk acceptance, and stop conditions defined?
- Does oversight continue after deployment and respond to changes, complaints, and incidents?
- Are generative AI and third-party services addressed explicitly?
- Have relevant legal or compliance staff mapped obligations for the organization’s jurisdictions, sector, and uses?
These checks are a way to test whether the policy can guide real decisions, not a claim that completing a list proves compliance or eliminates AI risk.
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